Schizophrenia is a complex psychiatric disorder with a heterogeneous clinical phenotype. It is probably one of the most costly psychiatric illnesses, yet the development of novel and safe antipsychotics lags behind. Amplifying the actions of endocannabinoids by inhibiting their enzymatic degradation has emerged as an alternative strategy to exploit the endocannabinoid system for possible clinical benefit. Here, we propose to apply a quantum-similarity approach to discover novel modulators (inhibitors) of fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase (MAGL), aiming to elevate endocannabinoid tone to relieve the negative symptoms of schizophrenia. Instead of blindly screening millions of compounds for novel modulators of the target of interest, our focused testing of 10-20 commercially available compounds per target (pathway, protein etc.) with predicted (inhibitory) activity by the modeling effort, allows us to quickly explore novel chemical spaces for therapeutic applications, specifically and accurately targeting elusive, hard to modulate protein- protein interactions previously considered unapproachable by current discovery methods. The modeling, virtual search, identification and rank ordering of novel classes of FAAH and MAGL inhibitors, as well as the de novo design of novel antipsychotics will be done by Gradient Biomodeling. The in vitro and in vivo experimental evaluation of the compounds will be done in the laboratory of Dr. Andrea Giuffrida, at the University of Texas Health Science Center at San Antonio.